Overview

Dataset statistics

Number of variables21
Number of observations5000
Missing cells31
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory820.4 KiB
Average record size in memory168.0 B

Variable types

Numeric16
Categorical2
Boolean3

Alerts

state has a high cardinality: 51 distinct valuesHigh cardinality
voice.messages is highly overall correlated with voice.planHigh correlation
intl.mins is highly overall correlated with intl.chargeHigh correlation
intl.charge is highly overall correlated with intl.minsHigh correlation
day.mins is highly overall correlated with day.chargeHigh correlation
day.charge is highly overall correlated with day.minsHigh correlation
eve.mins is highly overall correlated with eve.chargeHigh correlation
eve.charge is highly overall correlated with eve.minsHigh correlation
night.mins is highly overall correlated with night.chargeHigh correlation
night.charge is highly overall correlated with night.minsHigh correlation
voice.plan is highly overall correlated with voice.messagesHigh correlation
intl.plan is highly imbalanced (54.8%)Imbalance
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
voice.messages has 3678 (73.6%) zerosZeros
customer.calls has 1023 (20.5%) zerosZeros

Reproduction

Analysis started2023-01-29 10:09:25.115669
Analysis finished2023-01-29 10:12:17.986945
Duration2 minutes and 52.87 seconds
Software versionpandas-profiling vv3.6.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2500.5
Minimum1
Maximum5000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:18.086781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile250.95
Q11250.75
median2500.5
Q33750.25
95-th percentile4750.05
Maximum5000
Range4999
Interquartile range (IQR)2499.5

Descriptive statistics

Standard deviation1443.52
Coefficient of variation (CV)0.57729254
Kurtosis-1.2
Mean2500.5
Median Absolute Deviation (MAD)1250
Skewness0
Sum12502500
Variance2083750
MonotonicityStrictly increasing
2023-01-29T15:42:18.184155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
3331 1
 
< 0.1%
3338 1
 
< 0.1%
3337 1
 
< 0.1%
3336 1
 
< 0.1%
3335 1
 
< 0.1%
3334 1
 
< 0.1%
3333 1
 
< 0.1%
3332 1
 
< 0.1%
3330 1
 
< 0.1%
Other values (4990) 4990
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
5000 1
< 0.1%
4999 1
< 0.1%
4998 1
< 0.1%
4997 1
< 0.1%
4996 1
< 0.1%
4995 1
< 0.1%
4994 1
< 0.1%
4993 1
< 0.1%
4992 1
< 0.1%
4991 1
< 0.1%

state
Categorical

Distinct51
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
WV
 
158
MN
 
125
AL
 
124
ID
 
119
VA
 
118
Other values (46)
4356 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKS
2nd rowOH
3rd rowNJ
4th rowOH
5th rowOK

Common Values

ValueCountFrequency (%)
WV 158
 
3.2%
MN 125
 
2.5%
AL 124
 
2.5%
ID 119
 
2.4%
VA 118
 
2.4%
OH 116
 
2.3%
TX 116
 
2.3%
WY 115
 
2.3%
NY 114
 
2.3%
OR 114
 
2.3%
Other values (41) 3781
75.6%

Length

2023-01-29T15:42:18.262696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wv 158
 
3.2%
mn 125
 
2.5%
al 124
 
2.5%
id 119
 
2.4%
va 118
 
2.4%
oh 116
 
2.3%
tx 116
 
2.3%
wy 115
 
2.3%
ny 114
 
2.3%
or 114
 
2.3%
Other values (41) 3781
75.6%

Most occurring characters

ValueCountFrequency (%)
N 1081
 
10.8%
A 1059
 
10.6%
M 918
 
9.2%
I 768
 
7.7%
T 616
 
6.2%
D 576
 
5.8%
C 517
 
5.2%
O 509
 
5.1%
W 477
 
4.8%
V 467
 
4.7%
Other values (14) 3012
30.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10000
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1081
 
10.8%
A 1059
 
10.6%
M 918
 
9.2%
I 768
 
7.7%
T 616
 
6.2%
D 576
 
5.8%
C 517
 
5.2%
O 509
 
5.1%
W 477
 
4.8%
V 467
 
4.7%
Other values (14) 3012
30.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 10000
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1081
 
10.8%
A 1059
 
10.6%
M 918
 
9.2%
I 768
 
7.7%
T 616
 
6.2%
D 576
 
5.8%
C 517
 
5.2%
O 509
 
5.1%
W 477
 
4.8%
V 467
 
4.7%
Other values (14) 3012
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1081
 
10.8%
A 1059
 
10.6%
M 918
 
9.2%
I 768
 
7.7%
T 616
 
6.2%
D 576
 
5.8%
C 517
 
5.2%
O 509
 
5.1%
W 477
 
4.8%
V 467
 
4.7%
Other values (14) 3012
30.1%

area.code
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
area_code_415
2495 
area_code_408
1259 
area_code_510
1246 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters65000
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowarea_code_415
2nd rowarea_code_415
3rd rowarea_code_415
4th rowarea_code_408
5th rowarea_code_415

Common Values

ValueCountFrequency (%)
area_code_415 2495
49.9%
area_code_408 1259
25.2%
area_code_510 1246
24.9%

Length

2023-01-29T15:42:18.325217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-29T15:42:18.403934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
area_code_415 2495
49.9%
area_code_408 1259
25.2%
area_code_510 1246
24.9%

Most occurring characters

ValueCountFrequency (%)
a 10000
15.4%
e 10000
15.4%
_ 10000
15.4%
r 5000
7.7%
c 5000
7.7%
o 5000
7.7%
d 5000
7.7%
4 3754
 
5.8%
1 3741
 
5.8%
5 3741
 
5.8%
Other values (2) 3764
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 40000
61.5%
Decimal Number 15000
 
23.1%
Connector Punctuation 10000
 
15.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10000
25.0%
e 10000
25.0%
r 5000
12.5%
c 5000
12.5%
o 5000
12.5%
d 5000
12.5%
Decimal Number
ValueCountFrequency (%)
4 3754
25.0%
1 3741
24.9%
5 3741
24.9%
0 2505
16.7%
8 1259
 
8.4%
Connector Punctuation
ValueCountFrequency (%)
_ 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40000
61.5%
Common 25000
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10000
25.0%
e 10000
25.0%
r 5000
12.5%
c 5000
12.5%
o 5000
12.5%
d 5000
12.5%
Common
ValueCountFrequency (%)
_ 10000
40.0%
4 3754
 
15.0%
1 3741
 
15.0%
5 3741
 
15.0%
0 2505
 
10.0%
8 1259
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10000
15.4%
e 10000
15.4%
_ 10000
15.4%
r 5000
7.7%
c 5000
7.7%
o 5000
7.7%
d 5000
7.7%
4 3754
 
5.8%
1 3741
 
5.8%
5 3741
 
5.8%
Other values (2) 3764
 
5.8%

account.length
Real number (ℝ)

Distinct218
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.2586
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:18.482501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q173
median100
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.69456
Coefficient of variation (CV)0.39592174
Kurtosis-0.10162108
Mean100.2586
Median Absolute Deviation (MAD)27
Skewness0.10929112
Sum501293
Variance1575.6581
MonotonicityNot monotonic
2023-01-29T15:42:18.576684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 65
 
1.3%
87 59
 
1.2%
105 57
 
1.1%
93 57
 
1.1%
112 56
 
1.1%
101 55
 
1.1%
100 55
 
1.1%
86 55
 
1.1%
116 54
 
1.1%
103 54
 
1.1%
Other values (208) 4433
88.7%
ValueCountFrequency (%)
1 11
0.2%
2 2
 
< 0.1%
3 8
0.2%
4 3
 
0.1%
5 2
 
< 0.1%
6 2
 
< 0.1%
7 5
0.1%
8 2
 
< 0.1%
9 3
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
243 1
 
< 0.1%
238 1
 
< 0.1%
233 1
 
< 0.1%
232 2
< 0.1%
225 2
< 0.1%
224 2
< 0.1%
222 2
< 0.1%
221 1
 
< 0.1%
217 3
0.1%
216 1
 
< 0.1%

voice.plan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
3677 
True
1323 
ValueCountFrequency (%)
False 3677
73.5%
True 1323
 
26.5%
2023-01-29T15:42:18.655223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

voice.messages
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7552
Minimum0
Maximum52
Zeros3678
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:18.733369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317
95-th percentile37
Maximum52
Range52
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.546393
Coefficient of variation (CV)1.7467497
Kurtosis0.19912718
Mean7.7552
Median Absolute Deviation (MAD)0
Skewness1.3504932
Sum38776
Variance183.50477
MonotonicityNot monotonic
2023-01-29T15:42:18.827549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 3678
73.6%
31 83
 
1.7%
28 67
 
1.3%
29 67
 
1.3%
33 66
 
1.3%
24 64
 
1.3%
27 64
 
1.3%
30 58
 
1.2%
26 58
 
1.2%
32 57
 
1.1%
Other values (38) 738
 
14.8%
ValueCountFrequency (%)
0 3678
73.6%
4 1
 
< 0.1%
6 2
 
< 0.1%
8 2
 
< 0.1%
9 2
 
< 0.1%
10 4
 
0.1%
11 2
 
< 0.1%
12 11
 
0.2%
13 4
 
0.1%
14 9
 
0.2%
ValueCountFrequency (%)
52 1
 
< 0.1%
51 1
 
< 0.1%
50 2
 
< 0.1%
49 3
 
0.1%
48 5
 
0.1%
47 4
 
0.1%
46 8
0.2%
45 11
0.2%
44 9
0.2%
43 16
0.3%

intl.plan
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4527 
True
473 
ValueCountFrequency (%)
False 4527
90.5%
True 473
 
9.5%
2023-01-29T15:42:18.906104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

intl.mins
Real number (ℝ)

Distinct170
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.26178
Minimum0
Maximum20
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:18.984535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.7613957
Coefficient of variation (CV)0.2690952
Kurtosis0.65531661
Mean10.26178
Median Absolute Deviation (MAD)1.8
Skewness-0.20996629
Sum51308.9
Variance7.6253063
MonotonicityNot monotonic
2023-01-29T15:42:19.078716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.1 90
 
1.8%
9.8 88
 
1.8%
11.3 83
 
1.7%
11.4 81
 
1.6%
10.1 81
 
1.6%
10.9 80
 
1.6%
9.7 79
 
1.6%
10.6 78
 
1.6%
11 78
 
1.6%
10.5 78
 
1.6%
Other values (160) 4184
83.7%
ValueCountFrequency (%)
0 24
0.5%
0.4 1
 
< 0.1%
1.1 2
 
< 0.1%
1.3 1
 
< 0.1%
2 3
 
0.1%
2.1 2
 
< 0.1%
2.2 2
 
< 0.1%
2.4 1
 
< 0.1%
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
20 1
< 0.1%
19.7 2
< 0.1%
19.3 1
< 0.1%
19.2 1
< 0.1%
18.9 2
< 0.1%
18.7 1
< 0.1%
18.5 1
< 0.1%
18.4 1
< 0.1%
18.3 1
< 0.1%
18.2 2
< 0.1%

intl.calls
Real number (ℝ)

Distinct21
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4352
Minimum0
Maximum20
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:19.172897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4567882
Coefficient of variation (CV)0.55392951
Kurtosis3.2681836
Mean4.4352
Median Absolute Deviation (MAD)1
Skewness1.3606925
Sum22176
Variance6.0358081
MonotonicityNot monotonic
2023-01-29T15:42:19.251028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 992
19.8%
4 953
19.1%
2 743
14.9%
5 706
14.1%
6 495
9.9%
7 308
 
6.2%
1 265
 
5.3%
8 172
 
3.4%
9 148
 
3.0%
10 76
 
1.5%
Other values (11) 142
 
2.8%
ValueCountFrequency (%)
0 24
 
0.5%
1 265
 
5.3%
2 743
14.9%
3 992
19.8%
4 953
19.1%
5 706
14.1%
6 495
9.9%
7 308
 
6.2%
8 172
 
3.4%
9 148
 
3.0%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
0.1%
17 2
 
< 0.1%
16 7
 
0.1%
15 9
 
0.2%
14 6
 
0.1%
13 19
0.4%
12 23
0.5%
11 45
0.9%

intl.charge
Real number (ℝ)

Distinct170
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.771196
Minimum0
Maximum5.4
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:19.329585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.24
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.74551371
Coefficient of variation (CV)0.26902237
Kurtosis0.65598855
Mean2.771196
Median Absolute Deviation (MAD)0.48
Skewness-0.21028611
Sum13855.98
Variance0.55579069
MonotonicityNot monotonic
2023-01-29T15:42:19.423892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 90
 
1.8%
2.65 88
 
1.8%
3.05 83
 
1.7%
3.08 81
 
1.6%
2.73 81
 
1.6%
2.94 80
 
1.6%
2.62 79
 
1.6%
2.86 78
 
1.6%
2.97 78
 
1.6%
2.84 78
 
1.6%
Other values (160) 4184
83.7%
ValueCountFrequency (%)
0 24
0.5%
0.11 1
 
< 0.1%
0.3 2
 
< 0.1%
0.35 1
 
< 0.1%
0.54 3
 
0.1%
0.57 2
 
< 0.1%
0.59 2
 
< 0.1%
0.65 1
 
< 0.1%
0.68 1
 
< 0.1%
0.7 1
 
< 0.1%
ValueCountFrequency (%)
5.4 1
< 0.1%
5.32 2
< 0.1%
5.21 1
< 0.1%
5.18 1
< 0.1%
5.1 2
< 0.1%
5.05 1
< 0.1%
5 1
< 0.1%
4.97 1
< 0.1%
4.94 1
< 0.1%
4.91 2
< 0.1%

day.mins
Real number (ℝ)

Distinct1961
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.2889
Minimum0
Maximum351.5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:19.517648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile91.7
Q1143.7
median180.1
Q3216.2
95-th percentile271.105
Maximum351.5
Range351.5
Interquartile range (IQR)72.5

Descriptive statistics

Standard deviation53.894699
Coefficient of variation (CV)0.2989352
Kurtosis-0.021294471
Mean180.2889
Median Absolute Deviation (MAD)36.3
Skewness-0.011730827
Sum901444.5
Variance2904.6386
MonotonicityNot monotonic
2023-01-29T15:42:19.611828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
189.3 10
 
0.2%
154 10
 
0.2%
159.5 9
 
0.2%
180 9
 
0.2%
184.5 9
 
0.2%
174.5 9
 
0.2%
177.1 9
 
0.2%
183.4 8
 
0.2%
189.8 8
 
0.2%
215.6 8
 
0.2%
Other values (1951) 4911
98.2%
ValueCountFrequency (%)
0 2
< 0.1%
2.6 1
< 0.1%
6.6 1
< 0.1%
7.2 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
12.5 1
< 0.1%
17.6 1
< 0.1%
18.9 1
< 0.1%
19.5 1
< 0.1%
ValueCountFrequency (%)
351.5 1
< 0.1%
350.8 1
< 0.1%
346.8 1
< 0.1%
345.3 1
< 0.1%
338.4 1
< 0.1%
337.4 1
< 0.1%
335.5 1
< 0.1%
334.3 1
< 0.1%
332.9 1
< 0.1%
332.1 1
< 0.1%

day.calls
Real number (ℝ)

Distinct123
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.0294
Minimum0
Maximum165
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:19.721637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.831197
Coefficient of variation (CV)0.19825369
Kurtosis0.17856779
Mean100.0294
Median Absolute Deviation (MAD)13
Skewness-0.084890964
Sum500147
Variance393.27639
MonotonicityNot monotonic
2023-01-29T15:42:19.800192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 117
 
2.3%
102 113
 
2.3%
95 108
 
2.2%
94 104
 
2.1%
97 104
 
2.1%
100 102
 
2.0%
110 101
 
2.0%
112 101
 
2.0%
92 100
 
2.0%
108 100
 
2.0%
Other values (113) 3950
79.0%
ValueCountFrequency (%)
0 2
< 0.1%
30 1
 
< 0.1%
34 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
39 2
< 0.1%
40 2
< 0.1%
42 2
< 0.1%
44 4
0.1%
45 3
0.1%
ValueCountFrequency (%)
165 1
 
< 0.1%
163 1
 
< 0.1%
160 2
 
< 0.1%
158 3
0.1%
157 2
 
< 0.1%
156 3
0.1%
152 2
 
< 0.1%
151 7
0.1%
150 6
0.1%
149 2
 
< 0.1%

day.charge
Real number (ℝ)

Distinct1961
Distinct (%)39.3%
Missing7
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean30.653501
Minimum0
Maximum59.76
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:19.909999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.59
Q124.43
median30.62
Q336.75
95-th percentile46.094
Maximum59.76
Range59.76
Interquartile range (IQR)12.32

Descriptive statistics

Standard deviation9.1663556
Coefficient of variation (CV)0.29903128
Kurtosis-0.022889114
Mean30.653501
Median Absolute Deviation (MAD)6.17
Skewness-0.012595268
Sum153052.93
Variance84.022074
MonotonicityNot monotonic
2023-01-29T15:42:19.988554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.18 10
 
0.2%
32.18 10
 
0.2%
30.11 9
 
0.2%
31.37 9
 
0.2%
27.12 9
 
0.2%
30.6 9
 
0.2%
29.67 9
 
0.2%
36.65 8
 
0.2%
28.66 8
 
0.2%
31.45 8
 
0.2%
Other values (1951) 4904
98.1%
ValueCountFrequency (%)
0 2
< 0.1%
0.44 1
< 0.1%
1.12 1
< 0.1%
1.22 1
< 0.1%
1.33 1
< 0.1%
1.34 1
< 0.1%
2.13 1
< 0.1%
2.99 1
< 0.1%
3.21 1
< 0.1%
3.32 1
< 0.1%
ValueCountFrequency (%)
59.76 1
< 0.1%
59.64 1
< 0.1%
58.96 1
< 0.1%
58.7 1
< 0.1%
57.53 1
< 0.1%
57.36 1
< 0.1%
57.04 1
< 0.1%
56.83 1
< 0.1%
56.59 1
< 0.1%
56.46 1
< 0.1%

eve.mins
Real number (ℝ)

Distinct1876
Distinct (%)37.7%
Missing24
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean200.58033
Minimum0
Maximum363.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:20.098345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.35
Q1166.275
median201
Q3234.1
95-th percentile283.425
Maximum363.7
Range363.7
Interquartile range (IQR)67.825

Descriptive statistics

Standard deviation50.554637
Coefficient of variation (CV)0.25204185
Kurtosis0.051728388
Mean200.58033
Median Absolute Deviation (MAD)34
Skewness-0.012712675
Sum998087.7
Variance2555.7713
MonotonicityNot monotonic
2023-01-29T15:42:20.192515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230.9 10
 
0.2%
199.7 10
 
0.2%
169.9 10
 
0.2%
167.6 9
 
0.2%
216.5 9
 
0.2%
194 9
 
0.2%
187 9
 
0.2%
161.7 9
 
0.2%
223.5 9
 
0.2%
187.5 9
 
0.2%
Other values (1866) 4883
97.7%
(Missing) 24
 
0.5%
ValueCountFrequency (%)
0 1
< 0.1%
22.3 1
< 0.1%
31.2 1
< 0.1%
37.8 1
< 0.1%
41.7 1
< 0.1%
42.2 1
< 0.1%
42.5 1
< 0.1%
43.9 1
< 0.1%
47.3 2
< 0.1%
48.1 1
< 0.1%
ValueCountFrequency (%)
363.7 1
< 0.1%
361.8 1
< 0.1%
359.3 1
< 0.1%
354.2 1
< 0.1%
352.1 1
< 0.1%
351.6 1
< 0.1%
350.9 1
< 0.1%
350.5 1
< 0.1%
349.4 1
< 0.1%
348.9 1
< 0.1%

eve.calls
Real number (ℝ)

Distinct126
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.191
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:20.317978image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.826496
Coefficient of variation (CV)0.19788699
Kurtosis0.1173634
Mean100.191
Median Absolute Deviation (MAD)13
Skewness-0.020175203
Sum500955
Variance393.08994
MonotonicityNot monotonic
2023-01-29T15:42:20.443288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 115
 
2.3%
97 110
 
2.2%
91 110
 
2.2%
94 106
 
2.1%
103 106
 
2.1%
101 104
 
2.1%
96 100
 
2.0%
104 100
 
2.0%
102 99
 
2.0%
98 99
 
2.0%
Other values (116) 3951
79.0%
ValueCountFrequency (%)
0 1
 
< 0.1%
12 1
 
< 0.1%
36 1
 
< 0.1%
37 1
 
< 0.1%
38 1
 
< 0.1%
42 1
 
< 0.1%
43 1
 
< 0.1%
44 2
 
< 0.1%
45 1
 
< 0.1%
46 5
0.1%
ValueCountFrequency (%)
170 1
 
< 0.1%
169 1
 
< 0.1%
168 1
 
< 0.1%
164 1
 
< 0.1%
159 1
 
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
155 5
0.1%
154 4
0.1%
153 1
 
< 0.1%

eve.charge
Real number (ℝ)

Distinct1659
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.054322
Minimum0
Maximum30.91
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:20.569014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.0695
Q114.14
median17.09
Q319.9
95-th percentile24.112
Maximum30.91
Range30.91
Interquartile range (IQR)5.76

Descriptive statistics

Standard deviation4.2968433
Coefficient of variation (CV)0.2519504
Kurtosis0.051288785
Mean17.054322
Median Absolute Deviation (MAD)2.89
Skewness-0.010990328
Sum85271.61
Variance18.462862
MonotonicityNot monotonic
2023-01-29T15:42:20.678840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.9 15
 
0.3%
14.25 15
 
0.3%
16.12 14
 
0.3%
18.79 13
 
0.3%
16.97 13
 
0.3%
18.96 13
 
0.3%
19.41 12
 
0.2%
17.09 11
 
0.2%
16.8 11
 
0.2%
18.62 11
 
0.2%
Other values (1649) 4872
97.4%
ValueCountFrequency (%)
0 1
< 0.1%
1.9 1
< 0.1%
2.65 1
< 0.1%
3.21 1
< 0.1%
3.54 1
< 0.1%
3.59 1
< 0.1%
3.61 1
< 0.1%
3.73 1
< 0.1%
4.02 2
< 0.1%
4.09 1
< 0.1%
ValueCountFrequency (%)
30.91 1
< 0.1%
30.75 1
< 0.1%
30.54 1
< 0.1%
30.11 1
< 0.1%
29.93 1
< 0.1%
29.89 1
< 0.1%
29.83 1
< 0.1%
29.79 1
< 0.1%
29.7 1
< 0.1%
29.66 1
< 0.1%

night.mins
Real number (ℝ)

Distinct1853
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.39162
Minimum0
Maximum395
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:20.789019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile117.395
Q1166.9
median200.4
Q3234.7
95-th percentile283.405
Maximum395
Range395
Interquartile range (IQR)67.8

Descriptive statistics

Standard deviation50.527789
Coefficient of variation (CV)0.25214522
Kurtosis0.082359197
Mean200.39162
Median Absolute Deviation (MAD)33.8
Skewness0.019324917
Sum1001958.1
Variance2553.0575
MonotonicityNot monotonic
2023-01-29T15:42:20.914632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
188.2 11
 
0.2%
194.3 11
 
0.2%
186.2 11
 
0.2%
214.6 10
 
0.2%
208.9 10
 
0.2%
228.1 10
 
0.2%
210 9
 
0.2%
192.7 9
 
0.2%
193.6 9
 
0.2%
214.7 9
 
0.2%
Other values (1843) 4901
98.0%
ValueCountFrequency (%)
0 1
< 0.1%
23.2 1
< 0.1%
43.7 1
< 0.1%
45 1
< 0.1%
46.7 1
< 0.1%
47.4 1
< 0.1%
50.1 2
< 0.1%
50.9 1
< 0.1%
53.3 1
< 0.1%
54 1
< 0.1%
ValueCountFrequency (%)
395 1
< 0.1%
381.9 1
< 0.1%
381.6 1
< 0.1%
377.5 1
< 0.1%
367.7 1
< 0.1%
364.9 1
< 0.1%
364.3 1
< 0.1%
359.9 1
< 0.1%
355.1 1
< 0.1%
354.9 1
< 0.1%

night.calls
Real number (ℝ)

Distinct131
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.9192
Minimum0
Maximum175
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:21.040416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile132
Maximum175
Range175
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.958686
Coefficient of variation (CV)0.19974826
Kurtosis0.14443808
Mean99.9192
Median Absolute Deviation (MAD)13
Skewness0.0021328427
Sum499596
Variance398.34914
MonotonicityNot monotonic
2023-01-29T15:42:21.159261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 121
 
2.4%
102 109
 
2.2%
100 108
 
2.2%
104 106
 
2.1%
99 105
 
2.1%
103 104
 
2.1%
91 103
 
2.1%
94 103
 
2.1%
95 102
 
2.0%
98 102
 
2.0%
Other values (121) 3937
78.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
12 1
 
< 0.1%
33 1
 
< 0.1%
36 1
 
< 0.1%
38 2
< 0.1%
40 1
 
< 0.1%
41 1
 
< 0.1%
42 4
0.1%
43 1
 
< 0.1%
44 1
 
< 0.1%
ValueCountFrequency (%)
175 1
< 0.1%
170 1
< 0.1%
168 1
< 0.1%
166 1
< 0.1%
165 1
< 0.1%
164 1
< 0.1%
161 1
< 0.1%
160 1
< 0.1%
159 2
< 0.1%
158 2
< 0.1%

night.charge
Real number (ℝ)

Distinct1028
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.017732
Minimum0
Maximum17.77
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:21.276808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.28
Q17.51
median9.02
Q310.56
95-th percentile12.7505
Maximum17.77
Range17.77
Interquartile range (IQR)3.05

Descriptive statistics

Standard deviation2.2737627
Coefficient of variation (CV)0.25214352
Kurtosis0.082377615
Mean9.017732
Median Absolute Deviation (MAD)1.52
Skewness0.019286744
Sum45088.66
Variance5.1699966
MonotonicityNot monotonic
2023-01-29T15:42:21.386256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.66 19
 
0.4%
8.47 19
 
0.4%
10.8 18
 
0.4%
9.63 18
 
0.4%
8.15 18
 
0.4%
9.4 18
 
0.4%
10.26 18
 
0.4%
9.45 17
 
0.3%
10.49 17
 
0.3%
10.35 16
 
0.3%
Other values (1018) 4822
96.4%
ValueCountFrequency (%)
0 1
< 0.1%
1.04 1
< 0.1%
1.97 1
< 0.1%
2.03 1
< 0.1%
2.1 1
< 0.1%
2.13 1
< 0.1%
2.25 2
< 0.1%
2.29 1
< 0.1%
2.4 1
< 0.1%
2.43 1
< 0.1%
ValueCountFrequency (%)
17.77 1
< 0.1%
17.19 1
< 0.1%
17.17 1
< 0.1%
16.99 1
< 0.1%
16.55 1
< 0.1%
16.42 1
< 0.1%
16.39 1
< 0.1%
16.2 1
< 0.1%
15.98 1
< 0.1%
15.97 1
< 0.1%

customer.calls
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5704
Minimum0
Maximum9
Zeros1023
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-01-29T15:42:21.496212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3063633
Coefficient of variation (CV)0.83186662
Kurtosis1.4810955
Mean1.5704
Median Absolute Deviation (MAD)1
Skewness1.0424623
Sum7852
Variance1.7065852
MonotonicityNot monotonic
2023-01-29T15:42:21.574761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1786
35.7%
2 1127
22.5%
0 1023
20.5%
3 665
 
13.3%
4 252
 
5.0%
5 96
 
1.9%
6 34
 
0.7%
7 13
 
0.3%
9 2
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 1023
20.5%
1 1786
35.7%
2 1127
22.5%
3 665
 
13.3%
4 252
 
5.0%
5 96
 
1.9%
6 34
 
0.7%
7 13
 
0.3%
8 2
 
< 0.1%
9 2
 
< 0.1%
ValueCountFrequency (%)
9 2
 
< 0.1%
8 2
 
< 0.1%
7 13
 
0.3%
6 34
 
0.7%
5 96
 
1.9%
4 252
 
5.0%
3 665
 
13.3%
2 1127
22.5%
1 1786
35.7%
0 1023
20.5%

churn
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
False
4293 
True
707 
ValueCountFrequency (%)
False 4293
85.9%
True 707
 
14.1%
2023-01-29T15:42:21.668637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Interactions

2023-01-29T15:42:13.258278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:27.673754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:32.890563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:38.826692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:44.688541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:51.423584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:23.299769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:29.610089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:34.213092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:40.728854image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:14.793408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:47.655358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:52.385989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:56.592582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:42:00.818370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:42:09.034076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:42:13.337952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:27.793293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:33.011665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:38.919709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:44.902587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:51.569618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:23.395790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:29.725115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:34.304110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:41.999284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:16.104616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-29T15:39:51.050550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:23.097713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:29.384504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:34.032039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:40.544692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:12.100711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:44.638449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:52.213632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:56.419838image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:42:00.643619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:42:08.853953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:42:13.085376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:42:17.468680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:32.735999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:38.735684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:44.532240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:39:51.240546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:23.201747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:29.505066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:34.124069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:40:40.635833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:13.462654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:46.279019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:52.307843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:41:56.514427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:42:00.738024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:42:08.947028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-29T15:42:13.164291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-29T15:42:21.763723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0account.lengthvoice.messagesintl.minsintl.callsintl.chargeday.minsday.callsday.chargeeve.minseve.callseve.chargenight.minsnight.callsnight.chargecustomer.callsstatearea.codevoice.planintl.planchurn
Unnamed: 01.000-0.011-0.0420.008-0.0250.008-0.002-0.025-0.004-0.0190.002-0.002-0.013-0.013-0.0130.0160.0140.0000.0510.0000.033
account.length-0.0111.000-0.0100.0060.0180.0060.0050.0250.006-0.0020.008-0.0120.000-0.0060.000-0.0080.0000.0000.0000.0000.000
voice.messages-0.042-0.0101.0000.002-0.0100.0020.009-0.004-0.0010.015-0.0020.0240.0000.0070.000-0.0120.0250.0000.9980.0000.109
intl.mins0.0080.0060.0021.0000.0061.000-0.0230.007-0.0160.015-0.0110.009-0.0070.005-0.007-0.0160.0100.0000.0000.0000.058
intl.calls-0.0250.018-0.0100.0061.0000.006-0.0060.009-0.0050.0210.0060.011-0.013-0.000-0.013-0.0110.0000.0120.0000.0000.079
intl.charge0.0080.0060.0021.0000.0061.000-0.0230.007-0.0160.015-0.0110.009-0.0070.005-0.007-0.0160.0100.0000.0000.0000.058
day.mins-0.0020.0050.009-0.023-0.006-0.0231.0000.0060.933-0.0100.008-0.0110.0030.0030.003-0.0010.0000.0350.0310.0380.362
day.calls-0.0250.025-0.0040.0070.0090.0070.0061.0000.0110.0080.0110.0020.003-0.0040.002-0.0140.0050.0230.0000.0000.027
day.charge-0.0040.006-0.001-0.016-0.005-0.0160.9330.0111.000-0.0200.004-0.020-0.0060.003-0.006-0.0080.0000.0790.0490.1020.354
eve.mins-0.019-0.0020.0150.0150.0210.015-0.0100.008-0.0201.0000.0000.863-0.0210.015-0.021-0.0230.0000.0000.1300.0000.138
eve.calls0.0020.008-0.002-0.0110.006-0.0110.0080.0110.0040.0001.0000.0020.008-0.0160.0080.0120.0230.0000.0000.0000.000
eve.charge-0.002-0.0120.0240.0090.0110.009-0.0110.002-0.0200.8630.0021.000-0.0160.014-0.016-0.0180.0000.0000.0380.0000.083
night.mins-0.0130.0000.000-0.007-0.013-0.0070.0030.003-0.006-0.0210.008-0.0161.0000.0171.000-0.0150.0220.0000.0100.0460.037
night.calls-0.013-0.0060.0070.005-0.0000.0050.003-0.0040.0030.015-0.0160.0140.0171.0000.017-0.0010.0000.0000.0000.0000.000
night.charge-0.0130.0000.000-0.007-0.013-0.0070.0030.002-0.006-0.0210.008-0.0161.0000.0171.000-0.0150.0200.0000.0100.0450.037
customer.calls0.016-0.008-0.012-0.016-0.011-0.016-0.001-0.014-0.008-0.0230.012-0.018-0.015-0.001-0.0151.0000.0090.0080.0180.0220.312
state0.0140.0000.0250.0100.0000.0100.0000.0050.0000.0000.0230.0000.0220.0000.0200.0091.0000.0200.0000.0340.097
area.code0.0000.0000.0000.0000.0120.0000.0350.0230.0790.0000.0000.0000.0000.0000.0000.0080.0201.0000.0000.0250.000
voice.plan0.0510.0000.9980.0000.0000.0000.0310.0000.0490.1300.0000.0380.0100.0000.0100.0180.0000.0001.0000.0000.109
intl.plan0.0000.0000.0000.0000.0000.0000.0380.0000.1020.0000.0000.0000.0460.0000.0450.0220.0340.0250.0001.0000.258
churn0.0330.0000.1090.0580.0790.0580.3620.0270.3540.1380.0000.0830.0370.0000.0370.3120.0970.0000.1090.2581.000

Missing values

2023-01-29T15:42:17.609982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-29T15:42:17.798531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-29T15:42:17.952663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0statearea.codeaccount.lengthvoice.planvoice.messagesintl.planintl.minsintl.callsintl.chargeday.minsday.callsday.chargeeve.minseve.callseve.chargenight.minsnight.callsnight.chargecustomer.callschurn
01KSarea_code_415128yes25no10.032.70265.111045.07197.49916.78244.79111.011no
12OHarea_code_415107yes26no13.733.70161.612327.47195.510316.62254.410311.451no
23NJarea_code_415137no0no12.253.29243.411441.38121.211010.30162.61047.320no
34OHarea_code_40884no0yes6.671.78299.47150.961.9885.26196.9898.862no
45OKarea_code_41575no0yes10.132.73166.711328.34148.312212.61186.91218.413no
56ALarea_code_510118no0yes6.361.70223.49837.98220.610118.75203.91189.180no
67MAarea_code_510121yes24no7.572.03218.28837.09348.510829.62212.61189.573no
78MOarea_code_415147no0yes7.161.92157.07926.69103.1948.76211.8969.530no
89LAarea_code_408117no0no8.742.35184.59731.37351.68029.89215.8909.711no
910WVarea_code_415141yes37yes11.253.02258.68443.9622211118.87326.49714.690no
Unnamed: 0statearea.codeaccount.lengthvoice.planvoice.messagesintl.planintl.minsintl.callsintl.chargeday.minsday.callsday.chargeeve.minseve.callseve.chargenight.minsnight.callsnight.chargecustomer.callschurn
49904991NDarea_code_510140no0no7.562.03244.711541.6258.610121.98231.311210.411yes
49914992AZarea_code_51097no0no8.852.38252.68942.94340.39128.93256.56711.541yes
49924993MTarea_code_41583no0no10.362.78188.370Nan243.88820.72213.7799.620no
49934994WVarea_code_40873no0no11.563.11177.98930.24131.28211.15186.2898.383no
49944995NCarea_code_40875no0no6.971.86170.710129.02193.112616.41129.11045.811no
49954996HIarea_code_40850yes40no9.952.67235.712740.0722312618.96297.511613.392no
49964997WVarea_code_415152no0no14.723.97184.29031.31256.87321.83213.61139.613yes
49974998DCarea_code_41561no0no13.643.67140.68923.9172.812814.69212.4979.561no
49984999DCarea_code_510109no0no8.562.30188.86732.1171.79214.59224.48910.100no
49995000VTarea_code_41586yes34no9.3162.51129.410222267.110422.70154.81006.970no